from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				42





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):
   __lowerCAmelCase           =				True







   @register_to_config
   def __init__(     self						:						Optional[Any]   ,   lowerCamelCase_						:						int = 3   ,   lowerCamelCase_						:						int = 3   ,   lowerCamelCase_						:						Tuple[str] = ("DownEncoderBlock2D",)   ,   lowerCamelCase_						:						Tuple[str] = ("UpDecoderBlock2D",)   ,   lowerCamelCase_						:						Tuple[int] = (64,)   ,   lowerCamelCase_						:						int = 1   ,   lowerCamelCase_						:						str = "silu"   ,   lowerCamelCase_						:						int = 4   ,   lowerCamelCase_						:						int = 32   ,   lowerCamelCase_						:						int = 32   ,   lowerCamelCase_						:						float = 0.1_8_2_1_5   ,   ):





    """simple docstring"""


    super().__init__()

    # pass init params to Encoder
    UpperCamelCase			  =      Encoder(
        in_channels=lowerCamelCase_   ,   out_channels=lowerCamelCase_   ,   down_block_types=lowerCamelCase_   ,   block_out_channels=lowerCamelCase_   ,   layers_per_block=lowerCamelCase_   ,   act_fn=lowerCamelCase_   ,   norm_num_groups=lowerCamelCase_   ,   double_z=lowerCamelCase_   ,   )

    # pass init params to Decoder
    UpperCamelCase			  =      Decoder(
        in_channels=lowerCamelCase_   ,   out_channels=lowerCamelCase_   ,   up_block_types=lowerCamelCase_   ,   block_out_channels=lowerCamelCase_   ,   layers_per_block=lowerCamelCase_   ,   norm_num_groups=lowerCamelCase_   ,   act_fn=lowerCamelCase_   ,   )

    UpperCamelCase			  =      nn.Convad(2 * latent_channels   ,   2 * latent_channels   ,   1				)
    UpperCamelCase			  =      nn.Convad(lowerCamelCase_   ,   lowerCamelCase_   ,   1				)

    UpperCamelCase			  =      False
    UpperCamelCase			  =      False

    # only relevant if vae tiling is enabled
    UpperCamelCase			  =      self.config.sample_size
    UpperCamelCase			  =      (
        self.config.sample_size[0]
        if isinstance(self.config.sample_size   ,   (list, tuple)				)
        else self.config.sample_size
    )
    UpperCamelCase			  =      int(sample_size / (2 ** (len(self.config.block_out_channels				) - 1))				)
    UpperCamelCase			  =      0.2_5







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Optional[int]=False				):





    """simple docstring"""


    if isinstance(lowerCamelCase_   ,   (Encoder, Decoder)				):
     UpperCamelCase			  =      value







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						bool = True				):





    """simple docstring"""


    UpperCamelCase			  =      use_tiling







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    self.enable_tiling(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      True







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      False







   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      {}

    def fn_recursive_add_processors(lowerCamelCase_						:						str   ,   lowerCamelCase_						:						torch.nn.Module   ,   lowerCamelCase_						:						Dict[str, AttentionProcessor]				):
     if hasattr(lowerCamelCase_   ,   """set_processor"""				):
      UpperCamelCase			  =      module.processor

     for sub_name, child in module.named_children():
      fn_recursive_add_processors(f"""{name}.{sub_name}"""   ,   lowerCamelCase_   ,   lowerCamelCase_				)

     return processors

    for name, module in self.named_children():
     fn_recursive_add_processors(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)

    return processors







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Union[AttentionProcessor, Dict[str, AttentionProcessor]]				):





    """simple docstring"""


    UpperCamelCase			  =      len(self.attn_processors.keys()				)

    if isinstance(lowerCamelCase_   ,   lowerCamelCase_				) and len(lowerCamelCase_				) != count:
     raise ValueError(
         f"""A dict of processors was passed, but the number of processors {len(lowerCamelCase_				)} does not match the"""
         f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""				)

    def fn_recursive_attn_processor(lowerCamelCase_						:						str   ,   lowerCamelCase_						:						torch.nn.Module   ,   lowerCamelCase_						:						Optional[Any]				):
     if hasattr(lowerCamelCase_   ,   """set_processor"""				):
      if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
       module.set_processor(lowerCamelCase_				)
      else:
       module.set_processor(processor.pop(f"""{name}.processor"""				)				)

     for sub_name, child in module.named_children():
      fn_recursive_attn_processor(f"""{name}.{sub_name}"""   ,   lowerCamelCase_   ,   lowerCamelCase_				)

    for name, module in self.named_children():
     fn_recursive_attn_processor(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    self.set_attn_processor(AttnProcessor()				)







   @apply_forward_hook
   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						bool = True				):





    """simple docstring"""


    if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
     return self.tiled_encode(lowerCamelCase_   ,   return_dict=lowerCamelCase_				)

    if self.use_slicing and x.shape[0] > 1:
     UpperCamelCase			  =      [self.encoder(lowerCamelCase_				) for x_slice in x.split(1				)]
     UpperCamelCase			  =      torch.cat(lowerCamelCase_				)
    else:
     UpperCamelCase			  =      self.encoder(lowerCamelCase_				)

    UpperCamelCase			  =      self.quant_conv(lowerCamelCase_				)
    UpperCamelCase			  =      DiagonalGaussianDistribution(lowerCamelCase_				)

    if not return_dict:
     return (posterior,)

    return AutoencoderKLOutput(latent_dist=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						bool = True				):





    """simple docstring"""


    if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
     return self.tiled_decode(lowerCamelCase_   ,   return_dict=lowerCamelCase_				)

    UpperCamelCase			  =      self.post_quant_conv(lowerCamelCase_				)
    UpperCamelCase			  =      self.decoder(lowerCamelCase_				)

    if not return_dict:
     return (dec,)

    return DecoderOutput(sample=lowerCamelCase_				)







   @apply_forward_hook
   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						bool = True				):





    """simple docstring"""


    if self.use_slicing and z.shape[0] > 1:
     UpperCamelCase			  =      [self._decode(lowerCamelCase_				).sample for z_slice in z.split(1				)]
     UpperCamelCase			  =      torch.cat(lowerCamelCase_				)
    else:
     UpperCamelCase			  =      self._decode(lowerCamelCase_				).sample

    if not return_dict:
     return (decoded,)

    return DecoderOutput(sample=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      min(a.shape[2]   ,   b.shape[2]   ,   lowerCamelCase_				)
    for y in range(lowerCamelCase_				):
     UpperCamelCase			  =      a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
    return b







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      min(a.shape[3]   ,   b.shape[3]   ,   lowerCamelCase_				)
    for x in range(lowerCamelCase_				):
     UpperCamelCase			  =      a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
    return b







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						bool = True				):





    """simple docstring"""


    UpperCamelCase			  =      int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)				)
    UpperCamelCase			  =      int(self.tile_latent_min_size * self.tile_overlap_factor				)
    UpperCamelCase			  =      self.tile_latent_min_size - blend_extent

    # Split the image into 512x512 tiles and encode them separately.
    UpperCamelCase			  =      []
    for i in range(0   ,   x.shape[2]   ,   lowerCamelCase_				):
     UpperCamelCase			  =      []
     for j in range(0   ,   x.shape[3]   ,   lowerCamelCase_				):
      UpperCamelCase			  =      x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
      UpperCamelCase			  =      self.encoder(lowerCamelCase_				)
      UpperCamelCase			  =      self.quant_conv(lowerCamelCase_				)
      row.append(lowerCamelCase_				)
     rows.append(lowerCamelCase_				)
    UpperCamelCase			  =      []
    for i, row in enumerate(lowerCamelCase_				):
     UpperCamelCase			  =      []
     for j, tile in enumerate(lowerCamelCase_				):
      # blend the above tile and the left tile
      # to the current tile and add the current tile to the result row
      if i > 0:
       UpperCamelCase			  =      self.blend_v(rows[i - 1][j]   ,   lowerCamelCase_   ,   lowerCamelCase_				)
      if j > 0:
       UpperCamelCase			  =      self.blend_h(row[j - 1]   ,   lowerCamelCase_   ,   lowerCamelCase_				)
      result_row.append(tile[:, :, :row_limit, :row_limit]				)
     result_rows.append(torch.cat(lowerCamelCase_   ,   dim=3				)				)

    UpperCamelCase			  =      torch.cat(lowerCamelCase_   ,   dim=2				)
    UpperCamelCase			  =      DiagonalGaussianDistribution(lowerCamelCase_				)

    if not return_dict:
     return (posterior,)

    return AutoencoderKLOutput(latent_dist=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						bool = True				):





    """simple docstring"""


    UpperCamelCase			  =      int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)				)
    UpperCamelCase			  =      int(self.tile_sample_min_size * self.tile_overlap_factor				)
    UpperCamelCase			  =      self.tile_sample_min_size - blend_extent

    # Split z into overlapping 64x64 tiles and decode them separately.
    # The tiles have an overlap to avoid seams between tiles.
    UpperCamelCase			  =      []
    for i in range(0   ,   z.shape[2]   ,   lowerCamelCase_				):
     UpperCamelCase			  =      []
     for j in range(0   ,   z.shape[3]   ,   lowerCamelCase_				):
      UpperCamelCase			  =      z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
      UpperCamelCase			  =      self.post_quant_conv(lowerCamelCase_				)
      UpperCamelCase			  =      self.decoder(lowerCamelCase_				)
      row.append(lowerCamelCase_				)
     rows.append(lowerCamelCase_				)
    UpperCamelCase			  =      []
    for i, row in enumerate(lowerCamelCase_				):
     UpperCamelCase			  =      []
     for j, tile in enumerate(lowerCamelCase_				):
      # blend the above tile and the left tile
      # to the current tile and add the current tile to the result row
      if i > 0:
       UpperCamelCase			  =      self.blend_v(rows[i - 1][j]   ,   lowerCamelCase_   ,   lowerCamelCase_				)
      if j > 0:
       UpperCamelCase			  =      self.blend_h(row[j - 1]   ,   lowerCamelCase_   ,   lowerCamelCase_				)
      result_row.append(tile[:, :, :row_limit, :row_limit]				)
     result_rows.append(torch.cat(lowerCamelCase_   ,   dim=3				)				)

    UpperCamelCase			  =      torch.cat(lowerCamelCase_   ,   dim=2				)
    if not return_dict:
     return (dec,)

    return DecoderOutput(sample=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[torch.Generator] = None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      sample
    UpperCamelCase			  =      self.encode(lowerCamelCase_				).latent_dist
    if sample_posterior:
     UpperCamelCase			  =      posterior.sample(generator=lowerCamelCase_				)
    else:
     UpperCamelCase			  =      posterior.mode()
    UpperCamelCase			  =      self.decode(lowerCamelCase_				).sample

    if not return_dict:
     return (dec,)

    return DecoderOutput(sample=lowerCamelCase_				)

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     _SCREAMING_SNAKE_CASE 		=      """0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None       )		->	int:







 '''simple docstring'''
 if rng is None:
  UpperCamelCase			  =      random.Random()

 UpperCamelCase			  =      1
 for dim in shape:
  total_dims *= dim

 UpperCamelCase			  =      []
 for _ in range(UpperCamelCase_       ):
  values.append(rng.randint(0							,					vocab_size - 1       )       )

 UpperCamelCase			  =      np.array(UpperCamelCase_							,					dtype=jnp.intaa       ).reshape(UpperCamelCase_       )

 return output





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=None       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      ids_tensor(UpperCamelCase_							,					vocab_size=2							,					rng=UpperCamelCase_       )
 # make sure that at least one token is attended to for each batch
 UpperCamelCase			  =      1
 return attn_mask
@require_flax
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				()







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()

    # cut to half length & take max batch_size 3
    UpperCamelCase			  =      2
    UpperCamelCase			  =      inputs["""input_ids"""].shape[-1] // 2
    UpperCamelCase			  =      inputs["""input_ids"""][:max_batch_size, :sequence_length]

    UpperCamelCase			  =      jnp.ones_like(lowerCamelCase_				)
    UpperCamelCase			  =      attention_mask[:max_batch_size, :sequence_length]

    # generate max 5 tokens
    UpperCamelCase			  =      input_ids.shape[-1] + 5
    if config.eos_token_id is not None and config.pad_token_id is None:
     # hack to allow generate for models such as GPT2 as is done in `generate()`
     UpperCamelCase			  =      config.eos_token_id
    return config, input_ids, attention_mask, max_length







   @is_pt_flax_cross_test
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
     UpperCamelCase			  =      getattr(lowerCamelCase_   ,   lowerCamelCase_				)
     UpperCamelCase			  =      pt_model_class(lowerCamelCase_				).eval()
     UpperCamelCase			  =      load_flax_weights_in_pytorch_model(lowerCamelCase_   ,   flax_model.params				)

     UpperCamelCase			  =      flax_model.generate(lowerCamelCase_				).sequences
     UpperCamelCase			  =      pt_model.generate(torch.tensor(lowerCamelCase_   ,   dtype=torch.long				)				)

     if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
      UpperCamelCase			  =      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

     self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,   flax_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[0]   ,   input_ids.shape[0] * config.num_return_sequences				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0.8
    UpperCamelCase			  =      10
    UpperCamelCase			  =      0.3
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      2
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)





@require_flax
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
    UpperCamelCase			  =      FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

    UpperCamelCase			  =      """Hello world"""
    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   return_tensors="""np"""				).input_ids

    # typos are quickly detected (the correct argument is `do_sample`)
    with self.assertRaisesRegex(lowerCamelCase_   ,   """do_samples"""				):
     model.generate(lowerCamelCase_   ,   do_samples=lowerCamelCase_				)

    # arbitrary arguments that will not be used anywhere are also not accepted
    with self.assertRaisesRegex(lowerCamelCase_   ,   """foo"""				):
     UpperCamelCase			  =      {"""foo""": """bar"""}
     model.generate(lowerCamelCase_   ,   **lowerCamelCase_				)
